Last updated: 2020-11-11
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Knit directory: T47D_ZR75_DHT_StrippedSerum_RNASeq/analysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | 7263367 | Steve Ped | 2020-11-10 | Build site. |
| Rmd | fc4cd07 | Steve Ped | 2020-11-10 | Added ZR75 DGE |
| html | 3526304 | Steve Ped | 2020-11-10 | Build site. |
| Rmd | 7d3cf7f | Steve Ped | 2020-11-10 | Added count merging |
| html | ef81e6a | Steve Ped | 2020-11-10 | Build site. |
| html | 24c2e2f | Steve Ped | 2020-11-10 | Build site. |
| Rmd | 13c3089 | Steve Ped | 2020-11-10 | Recompiled pre-alignment qc |
| html | 6985f16 | Steve Ped | 2020-11-09 | Build site. |
| Rmd | 30ecf4f | Steve Ped | 2020-11-09 | Corrected errors |
| html | c8e1455 | Steve Ped | 2020-11-09 | Build site. |
| Rmd | fda3bb2 | Steve Ped | 2020-11-09 | Rewrote qc_raw |
| html | ea635ab | Steve Ped | 2020-11-09 | Build site. |
| Rmd | 8d91bfc | Steve Ped | 2020-11-09 | Rewrote qc_raw |
| html | 3502d11 | Steve Ped | 2020-11-09 | Build site. |
| Rmd | 9d03800 | Steve Ped | 2020-11-09 | Added tabsets for both runs |
| Rmd | 6f7af53 | Steve Pederson | 2020-11-06 | Initial Commit |
library(ngsReports)
library(tidyverse)
library(yaml)
library(scales)
library(pander)
library(glue)
library(plotly)
library(msa)
panderOptions("table.split.table", Inf)
panderOptions("big.mark", ",")
theme_set(theme_bw())
config <- here::here("config/config.yml") %>%
read_yaml()
suffix <- paste0(config$tags$tag, config$ext)
sp <- config$ref$species %>%
str_replace("(^[a-z])[a-z]*_([a-z]+)", "\\1\\2") %>%
str_to_title()
runRegEx <- paste(str_split(config$runs, " ")[[1]], collapse = "|")
samples <- config$samples %>%
here::here() %>%
read_tsv() %>%
mutate(
R1 = paste0(sample, config$tags$r1, suffix),
R2 = paste0(sample, config$tags$r2, suffix),
) %>%
pivot_longer(
cols = c("R1", "R2"),
names_to = "Reads",
values_to = "Filename"
) %>%
mutate_if(
function(x){length(unique(x)) < length(x)},
as.factor
)
config$analysis <- config$analysis %>%
lapply(intersect, y = colnames(samples)) %>%
.[vapply(., length, integer(1)) > 0]
if (length(config$analysis)) {
samples <- samples %>%
unite(
col = group,
any_of(as.character(unlist(config$analysis))),
sep = "_", remove = FALSE
)
} else {
samples$group <- samples$Filename
}
group_cols <- hcl.colors(
n = length(unique(samples$group)),
palette = "Zissou 1"
) %>%
setNames(unique(samples$group))
fh <- round(6 + nrow(samples) / 7, 0)
rawFqc <- here::here("data/raw/FastQC") %>%
list.files(pattern = "fastqc.zip", full.names = TRUE, recursive = TRUE) %>%
FastqcDataList() %>%
.[fqName(.) %in% samples$Filename]
for (i in seq_along(rawFqc)){
run <- str_extract(path(rawFqc[[i]]), runRegEx)
rawFqc[[i]]@Summary$Filename <- paste(run, rawFqc[[i]]@Summary$Filename, sep = "/")
}
plotSummary(rawFqc)
Overall summary of FastQC reports
A total of 64 libraries were contained in this dataset, with read totals ranging between 5,699,001 and 28,623,352 reads.
Across all libraries, reads were between 35 and 101 bases. This does indicate some read trimming had been performed prior to that undertaken here.
plotReadTotals(rawFqc, pattern = suffix, usePlotly = TRUE)
Library Sizes for all supplied fastq files. Any samples run as multiple libraries are shown as the supplied multiple libraries and have not been merged.
plotBaseQuals(
rawFqc,
pattern = suffix,
usePlotly = TRUE,
dendrogram = TRUE,
cluster = TRUE
)
Mean sequencing quality scores at each base position for each library
GC content patterns for both R1 and R2 reads were highly reminiscent of those seen when rRNA has not been completely removed.
plotGcContent(rawFqc[grepl("R1", fqName(rawFqc))], plotType = "line", usePlotly = TRUE, gcType = "Trans")
GC content for all R1 libraries
plotGcContent(rawFqc[grepl("R2", fqName(rawFqc))], plotType = "line", usePlotly = TRUE, gcType = "Trans")
GC content for all R2 libraries
plotly::ggplotly(
getModule(rawFqc, module = "Per_base_sequence_content") %>%
mutate(Base = fct_inorder(Base)) %>%
group_by(Base) %>%
mutate(
across(c("A", "C", "G", "T"), function(x){x - mean(x)})
) %>%
pivot_longer(
cols = c("A", "C", "G", "T"),
names_to = "Nuc",
values_to = "resid"
) %>%
separate(Filename, into = c("Run", "Filename"), sep = "/") %>%
left_join(samples) %>%
unite(Filename, Run, Filename, sep = "/") %>%
ggplot(
aes(Base, resid, group = Filename, colour = group)
) +
geom_line() +
facet_wrap(~Nuc) +
scale_colour_manual(values = group_cols) +
labs(
x = "Read Position", y = "Residual", colour = "Group"
)
)
Base and Position specific residuals for each sample. The mean base content at each position was calculated for each nucleotide, and the sample-specific residuals calculated.
plotAdapterContent(
x = rawFqc,
pattern = suffix,
usePlotly = TRUE,
dendrogram = TRUE,
cluster = TRUE
)
Total Adapter Content for each sample shown by starting position in the read.
os <- suppressMessages(getModule(rawFqc, "Over"))
os_fh <- min(20, 6 + nrow(os) / 20)
if (nrow(os)){
if (length(unique(os$Filename)) > 1){
suppressMessages(
plotOverrep(
x = rawFqc,
pattern = suffix,
usePlotly = TRUE,
dendrogram = TRUE,
cluster = TRUE
)
)
}
}
Summary of over-represented sequences across all libraries
os %>%
group_by(Sequence, Possible_Source) %>%
summarise(
`Found in` = n(),
Total = sum(Count),
`Largest Percent` = glue("{round(max(Percentage), 2)}%")
) %>%
ungroup() %>%
arrange(desc(Total)) %>%
dplyr::filter(Total > 5e5) %>%
mutate(Rank = seq_along(Total)) %>%
dplyr::select(Rank, everything()) %>%
pander(
caption = "*Summary of most abundant over-represented sequences within the raw data.*"
)
| Rank | Sequence | Possible_Source | Found in | Total | Largest Percent |
|---|---|---|---|---|---|
| 1 | CCTTAGGCAACCTGGTGGTCCCCCGCTCCCGGGAGGTCACCATATTGATG | No Hit | 32 | 2,775,012 | 0.88% |
| 2 | CTGGAGTCTTGGAAGCTTGACTACCCTACGTTCTCCTACAAATGGACCTT | No Hit | 32 | 1,869,452 | 0.64% |
| 3 | CTCCGTTTCCGACCTGGGCCGGTTCACCCCTCCTTAGGCAACCTGGTGGT | No Hit | 32 | 1,667,442 | 0.53% |
| 4 | CTCCTTAGGCAACCTGGTGGTCCCCCGCTCCCGGGAGGTCACCATATTGA | No Hit | 32 | 1,551,495 | 0.63% |
| 5 | CCCCTCCTTAGGCAACCTGGTGGTCCCCCGCTCCCGGGAGGTCACCATAT | No Hit | 32 | 1,505,323 | 0.53% |
| 6 | CCCTCCTTAGGCAACCTGGTGGTCCCCCGCTCCCGGGAGGTCACCATATT | No Hit | 32 | 1,382,213 | 0.5% |
| 7 | CTTAGGCAACCTGGTGGTCCCCCGCTCCCGGGAGGTCACCATATTGATGC | No Hit | 30 | 1,173,525 | 0.47% |
| 8 | CCCAAACCCACTCCACCTTACTACCAGACAACCTTAGCCAAACCATTTAC | No Hit | 16 | 1,082,742 | 0.56% |
| 9 | CTTGGTTATAATTTTTCATCTTTCCCTTGCGGTACTATATCTATTGCGCC | No Hit | 30 | 1,075,631 | 0.51% |
| 10 | CTCTCTACAAGGTTTTTTCCTAGTGTCCAAAGAGCTGTTCCTCTTTGGAC | No Hit | 32 | 1,048,457 | 0.35% |
| 11 | CCAGGCTGGAGTGCAGTGGCTATTCACAGGCGCGATCCCACTACTGATCA | No Hit | 26 | 1,042,034 | 0.53% |
| 12 | CCTCCTTAGGCAACCTGGTGGTCCCCCGCTCCCGGGAGGTCACCATATTG | No Hit | 32 | 989,640 | 0.31% |
| 13 | CTTATTTCTCTTGTCCTTTCGTACAGGGAGGAATTTGAAGTAGATAGAAA | No Hit | 32 | 925,237 | 0.35% |
| 14 | CTGAACTCCTCACACCCAATTGGACCAATCTATCACCCTATAGAAGAACT | No Hit | 32 | 893,807 | 0.3% |
| 15 | CTGGGCTGTAGTGCGCTATGCCGATCGGGTGTCCGCACTAAGTTCGGCAT | No Hit | 30 | 830,226 | 0.26% |
| 16 | CTCTAGAATAGGATTGCGCTGTTATCCCTAGGGTAACTTGTTCCGTTGGT | No Hit | 31 | 828,390 | 0.27% |
| 17 | CGGGGGAAGGCGCTTTGTGAAGTAGGCCTTATTTCTCTTGTCCTTTCGTA | No Hit | 32 | 800,786 | 0.37% |
| 18 | CCCAGCTACTCGGGAGGCTGAGGCTGGAGGATCGCTTGAGTCCAGGAGTT | No Hit | 32 | 797,383 | 0.25% |
| 19 | CTCCGAGGTCGCCCCAACCGAAATTTTTAATGCAGGTTTGGTAGTTTAGG | No Hit | 32 | 778,604 | 0.25% |
| 20 | CTGGTTTCGGGGGTCTTAGCTTTGGCTCTCCTTGCAAAGTTATTTCTAGT | No Hit | 30 | 777,092 | 0.29% |
| 21 | CTTGAGTCCAGGAGTTCTGGGCTGTAGTGCGCTATGCCGATCGGGTGTCC | No Hit | 30 | 768,861 | 0.23% |
| 22 | CGTTGGTCAAGTTATTGGATCAATTGAGTATAGTAGTTCGCTTTGACTGG | No Hit | 30 | 740,767 | 0.22% |
| 23 | CCCAAACCCACTCCACCCTACTACCAGACAACCTTAGCCAAACCATTTAC | No Hit | 16 | 729,714 | 0.4% |
| 24 | GTTCTGGGCTGTAGTGCGCTATGCCGATCGGGTGTCCGCACTAAGTTCGG | No Hit | 30 | 725,496 | 0.23% |
| 25 | CTCAGGCTGGAGTGCAGTGGCTATTCACAGGCGCGATCCCACTACTGATC | No Hit | 25 | 719,900 | 0.27% |
| 26 | CAGGCTGGAGTGCAGTGGCTATTCACAGGCGCGATCCCACTACTGATCAG | No Hit | 25 | 699,697 | 0.27% |
| 27 | CTTTAATCGTTGAACAAACGAACCTTTAATAGCGGCTGCACCATCGGGAT | No Hit | 31 | 691,249 | 0.21% |
| 28 | CTACAATCAACCAACAAGTCATTATTACCCTCACTGTCAACCCAACACAG | No Hit | 32 | 664,586 | 0.21% |
| 29 | CCCTGTTCTTGGGTGGGTGTGGGTATAATGCTAAGTTGAGATGATATCAT | No Hit | 15 | 659,116 | 0.47% |
| 30 | CCCTGTTCTTGGGTGGGTGTGGGTATAATACTAAGTTGAGATGATATCAT | No Hit | 15 | 641,218 | 0.4% |
| 31 | GTCCAATTGGGTGTGAGGAGTTCAGTTATATGTTTGGGATTTTTTAGGTA | No Hit | 25 | 620,097 | 0.29% |
| 32 | CTCTAAATCCCCTTGTAAATTTAACTGTTAGTCCAAAGAGGAACAGCTCT | No Hit | 28 | 617,871 | 0.23% |
| 33 | CTCATTTGGATGTGTCTGGAGTCTTGGAAGCTTGACTACCCTACGTTCTC | No Hit | 29 | 610,661 | 0.24% |
| 34 | CCTCGATGTTGGATCAGGACATCCCGATGGTGCAGCCGCTATTAAAGGTT | No Hit | 29 | 604,876 | 0.19% |
| 35 | GTATAATACTAAGTTGAGATGATATCATTTACGGGGGAAGGCGCTTTGTG | No Hit | 16 | 600,508 | 0.44% |
| 36 | CGCTTGAGTCCAGGAGTTCTGGGCTGTAGTGCGCTATGCCGATCGGGTGT | No Hit | 29 | 589,770 | 0.21% |
| 37 | CTCAGACCGCGTTCTCTCCCTCTCACTCCCCAATACGGAGAGAAGAACGA | No Hit | 23 | 589,407 | 0.31% |
| 38 | GCTACCTTTGCACGGTTAGGGTACCGCGGCCGTTAAACATGTGTCACTGG | No Hit | 23 | 564,289 | 0.21% |
| 39 | CACCAGGTTGCCTAAGGAGGGGTGAACCGGCCCAGGTCGGAAACGGAGCA | No Hit | 26 | 551,677 | 0.21% |
| 40 | GTGGGTATAATGCTAAGTTGAGATGATATCATTTACGGGGGAAGGCGCTT | No Hit | 15 | 547,997 | 0.37% |
| 41 | CTTTCGTACAGGGAGGAATTTGAAGTAGATAGAAACCGACCTGGATTACT | No Hit | 26 | 543,299 | 0.22% |
| 42 | CGCGTGCCTGTAGTCCCAGCTACTCGGGAGGCTGAGGCTGGAGGATCGCT | No Hit | 27 | 517,875 | 0.17% |
| 43 | GTAAGATTTGCCGAGTTCCTTTTACTTTTTTTAACCTTTCCTTATGGGCA | No Hit | 15 | 501,227 | 0.33% |
os %>%
group_by(Sequence, Possible_Source) %>%
summarise(
Total = sum(Count), .groups = "drop"
) %>%
arrange(desc(Total)) %>%
pull("Sequence") %>%
setNames(paste0("os", seq_along(.))) %>%
str_subset("TGGTCCCCCGCTCCCGGGAGG") %>%
as("DNAStringSet") %>%
msa() %>%
print(show = "complete")
use default substitution matrix
MsaDNAMultipleAlignment with 9 rows and 66 columns
aln
[1] CCCCTCCTTAGGCAACCTGGTGGTCCCCCGCTCCCGGGAGGTCACCATAT----------------
[2] -CCCTCCTTAGGCAACCTGGTGGTCCCCCGCTCCCGGGAGGTCACCATATT---------------
[3] --CCTCCTTAGGCAACCTGGTGGTCCCCCGCTCCCGGGAGGTCACCATATTG--------------
[4] ---CTCCTTAGGCAACCTGGTGGTCCCCCGCTCCCGGGAGGTCACCATATTGA-------------
[5] -----CCTTAGGCAACCTGGTGGTCCCCCGCTCCCGGGAGGTCACCATATTGATG-----------
[6] -----ACTTAGGCAACCTGGTGGTCCCCCGCTCCCGGGAGGTCACCATATTGATG-----------
[7] ------CTTAGGCAACCTGGTGGTCCCCCGCTCCCGGGAGGTCACCATATTGATGC----------
[8] ------ATTAGGCAACCTGGTGGTCCCCCGCTCCCGGGAGGTCACCATATTGATGC----------
[9] ----------------CTGGTGGTCCCCCGCTCCCGGGAGGTCACCATATTGATGCCGAACTTAGT
Con -----CCTTAGGCAACCTGGTGGTCCCCCGCTCCCGGGAGGTCACCATATTGATG-----------
A BLAST search of the above manually selected sequences (which are clearly the same sequence) revealed this to be a perfect match to 39 genomic locations such as RPS29, RN7SL2 and RN7SL396P. This RNA is likely to have some association with rRNA.
os %>%
group_by(Sequence, Possible_Source) %>%
summarise(
Total = sum(Count), .groups = "drop"
) %>%
ungroup() %>%
arrange(desc(Total)) %>%
pull("Sequence") %>%
setNames(paste0("os", seq_along(.))) %>%
str_subset("TATTTCTCTTGTCCTTTCGTA") %>%
as("DNAStringSet") %>%
msa() %>%
print(show = "complete")
use default substitution matrix
MsaDNAMultipleAlignment with 7 rows and 77 columns
aln (1..75)
[1] CGGGGGAAGGCGCTTTGTGAAGTAGGCCTTATTTCTCTTGTCCTTTCGTA-------------------------
[2] AGGGGGAAGGCGCTTTGTGAAGTAGGCCTTATTTCTCTTGTCCTTTCGTA-------------------------
[3] -GGGGGAAGGCGCTTTGTGAAGTAGGCCTTATTTCTCTTGTCCTTTCGTAC------------------------
[4] -AGGGGAAGGCGCTTTGTGAAGTAGGCCTTATTTCTCTTGTCCTTTCGTAC------------------------
[5] ---------------------------CTTATTTCTCTTGTCCTTTCGTACAGGGAGGAATTTGAAGTAGATAGA
[6] --------------------------CCTTATTTCTCTTGTCCTTTCGTACAGGGAGGAATTTGAAGTAGATAGA
[7] -------------------------GCCTTATTTCTCTTGTCCTTTCGTACAGGGAGGAATTTGAAGTAGATAGA
Con -?GGGGAAGGCGCTTTGTGAAGTAGGCCTTATTTCTCTTGTCCTTTCGTAC------------------------
aln (76..77)
[1] --
[2] --
[3] --
[4] --
[5] AA
[6] A-
[7] --
Con --
A manual search using additional over-represented sequences which did not align to the above consensus, revealed matches to additional SRP RNAs and 7S RNA
As such, rRNA may present some issues for this dataset as these sequences will not be removed by adapter removal. Alignment to a rRNA genome may be of benefit.
sessionInfo()
R version 4.0.2 (2020-06-22)
Platform: x86_64-conda_cos6-linux-gnu (64-bit)
Running under: Red Hat Enterprise Linux Server 7.7 (Maipo)
Matrix products: default
BLAS/LAPACK: /hpcfs/users/a1018048/T47D_ZR75_DHT_StrippedSerum_RNASeq/.snakemake/conda/090a90df/lib/libopenblasp-r0.3.10.so
locale:
[1] LC_CTYPE=en_AU.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_AU.UTF-8 LC_COLLATE=en_AU.UTF-8
[5] LC_MONETARY=en_AU.UTF-8 LC_MESSAGES=en_AU.UTF-8
[7] LC_PAPER=en_AU.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_AU.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats4 parallel stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] msa_1.22.0 Biostrings_2.58.0 XVector_0.30.0
[4] IRanges_2.24.0 S4Vectors_0.28.0 plotly_4.9.2.1
[7] glue_1.4.2 pander_0.6.3 scales_1.1.1
[10] yaml_2.2.1 forcats_0.5.0 stringr_1.4.0
[13] dplyr_1.0.2 purrr_0.3.4 readr_1.4.0
[16] tidyr_1.1.2 tidyverse_1.3.0 ngsReports_1.6.0
[19] tibble_3.0.4 ggplot2_3.3.2 BiocGenerics_0.36.0
[22] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] colorspace_1.4-1 hwriter_1.3.2
[3] ellipsis_0.3.1 rprojroot_1.3-2
[5] GenomicRanges_1.42.0 ggdendro_0.1.22
[7] fs_1.5.0 rstudioapi_0.11
[9] farver_2.0.3 ggrepel_0.8.2
[11] DT_0.16 fansi_0.4.1
[13] lubridate_1.7.9 xml2_1.3.2
[15] leaps_3.1 knitr_1.30
[17] jsonlite_1.7.1 Rsamtools_2.6.0
[19] broom_0.7.2 cluster_2.1.0
[21] dbplyr_2.0.0 png_0.1-7
[23] compiler_4.0.2 httr_1.4.2
[25] backports_1.2.0 assertthat_0.2.1
[27] Matrix_1.2-18 lazyeval_0.2.2
[29] cli_2.1.0 later_1.1.0.1
[31] htmltools_0.5.0 tools_4.0.2
[33] gtable_0.3.0 GenomeInfoDbData_1.2.4
[35] reshape2_1.4.4 FactoMineR_2.3
[37] ShortRead_1.48.0 Rcpp_1.0.4.6
[39] Biobase_2.50.0 cellranger_1.1.0
[41] vctrs_0.3.4 crosstalk_1.1.0.1
[43] xfun_0.19 ps_1.4.0
[45] rvest_0.3.6 lifecycle_0.2.0
[47] zlibbioc_1.36.0 MASS_7.3-53
[49] zoo_1.8-8 hms_0.5.3
[51] promises_1.1.1 MatrixGenerics_1.2.0
[53] SummarizedExperiment_1.20.0 RColorBrewer_1.1-2
[55] latticeExtra_0.6-29 stringi_1.5.3
[57] highr_0.8 BiocParallel_1.24.0
[59] GenomeInfoDb_1.26.0 rlang_0.4.8
[61] pkgconfig_2.0.3 matrixStats_0.57.0
[63] bitops_1.0-6 evaluate_0.14
[65] lattice_0.20-41 labeling_0.4.2
[67] GenomicAlignments_1.26.0 htmlwidgets_1.5.2
[69] tidyselect_1.1.0 here_0.1
[71] plyr_1.8.6 magrittr_1.5
[73] R6_2.5.0 generics_0.1.0
[75] DelayedArray_0.16.0 DBI_1.1.0
[77] pillar_1.4.6 haven_2.3.1
[79] whisker_0.4 withr_2.3.0
[81] scatterplot3d_0.3-41 RCurl_1.98-1.2
[83] modelr_0.1.8 crayon_1.3.4
[85] rmarkdown_2.5 jpeg_0.1-8.1
[87] grid_4.0.2 readxl_1.3.1
[89] data.table_1.13.2 git2r_0.27.1
[91] reprex_0.3.0 digest_0.6.27
[93] flashClust_1.01-2 httpuv_1.5.4
[95] munsell_0.5.0 viridisLite_0.3.0